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Event-level prediction of urban crime reveals a signature of enforcement bias in US cities

Abstract

Policing efforts to thwart crime typically rely on criminal infraction reports, which implicitly manifest a complex relationship between crime, policing and society. As a result, crime prediction and predictive policing have stirred controversy, with the latest artificial intelligence-based algorithms producing limited insight into the social system of crime. Here we show that, while predictive models may enhance state power through criminal surveillance, they also enable surveillance of the state by tracing systemic biases in crime enforcement. We introduce a stochastic inference algorithm that forecasts crime by learning spatio-temporal dependencies from event reports, with a mean area under the receiver operating characteristic curve of ~90% in Chicago for crimes predicted per week within ~1,000 ft. Such predictions enable us to study perturbations of crime patterns that suggest that the response to increased crime is biased by neighbourhood socio-economic status, draining policy resources from socio-economically disadvantaged areas, as demonstrated in eight major US cities.

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Fig. 1: Crime data and modelling approach.
Fig. 2: Predictive performance of Granger networks.
Fig. 3: Estimating bias.
Fig. 4: Prediction of property and violent crimes across major US cities and the dependency of the perturbation response on the SES of local neighbourhoods.
Fig. 5: The PAI and PEI calculated for seven metropolitan cities.
Fig. 6: Direct observations of the differential response of arrest rate changes with SES variables.

Data availability

Crime incident data used in this study are in the public domain. The web links for the data sources for seven out of the eight cities considered here are: opendata.atlantapd.org, data.austintexas.gov, data.detroitmi.gov, data.lacity.org, www.opendata.philly.org, data.sfgov.org, and data.cityofchicago.org, and for Portland the data along with the leader-board data for the forecasting challenge were obtained from nij.ojp.gov.

Code availability

Software with source code is available at https://github.com/zeroknowledgediscovery/Cynet, and the current version of the software may be referenced by https://doi.org/10.5281/zenodo.5730613. Any questions on implementation should be directed to the corresponding author.

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Acknowledgements

Our work greatly benefited from discussion of everyone who participated in our workshop series on crime prediction at the Neubauer Collegium for culture and society (https://neubauercollegium.uchicago.edu/events/uc/crimes_of_prediction_workshop/), and with those with whom we had extended conversations to ground and refine our modelling approach.

Data were provided by the City of Chicago data portal at https://data.cityofchicago.org. The City of Chicago (‘City’) voluntarily provides the data on this website as a service to the public. The City makes no warranty, representation, or guarantee as to the content, accuracy, timeliness, or completeness of any of the data provided at this website (https://www.chicago.gov/city/en/narr/foia/data_disclaimer.html), and the authors of this study are solely responsible for the opinions and conclusions expressed in this study. Sources of the crime incidence data for the other cities are tabulated in Table 1. Socio-economic data for metropolitan areas were obtained from https://www.census.gov.

This work is funded in part by the Defense Sciences Office of the Defense Advanced Research Projects Agency projects HR00111890043/P00004 and W911NF2010302, and the Neubauer Collegium for Culture and Society through the Faculty Initiated Research Program 2017. The claims made in this study do not necessarily reflect the position or the policy of the sponsors, and no official endorsement should be inferred.

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Contributions

Y.H. and I.C. worked out key mathematical details of the Granger network framework. V.R., T.L., Y.H. and I.C. implemented the algorithms and generated results. Y.H., V.R. and T.L. contributed equally in realizing the current implementation of the software. I.C. generated the visualizations in this study. J.E. provided key insights into modelling and interpreting social dynamics. J.E. and I.C. conceived and designed the research, and wrote the paper.

Corresponding author

Correspondence to Ishanu Chattopadhyay.

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Nature Human Behaviour thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Out of Sample Predictive Performance over the Years.

We show that the predictive performance is very stable, and variation in mean AUC is limited to the third place of decimal, at least when analyzing the last few years (4 years shown).

Extended Data Fig. 2 Comparison of Predicted vs Actual Sample Paths in Time and Frequency Domains.

Panels a, c and e show that the predicted and actual sample paths are pretty close for different years, when compared over the first 150 days of each year. Panels b, d and f show that the Fourier coefficients match up pretty well as well. More importantly, while our models do not explicitly incorporate any periodic elements that are being tuned, we still manage to capture the weekly, (approximately) biweekly and longer periodic regularities.

Extended Data Fig. 3 Perturbation Effects Across Variables.

We see that the decrease of violent crimes from increase of property crimes are localized in disadvantaged neighborhoods (panel g). Similarly, the decrease of property crimes from increase of violent crimes is also localized to disadvantaged neighborhoods (panel a), as well as the decreased violent crimes from increased arrests (panel k). We see a weaker localization for the corresponding increases in crime rates under similar perturbations. Looking at other pairs of variables under perturbation (rest of the panels), we generally do not see a very prominent correspondence with the distribution of socio-economic indicators. It seems crimes (and particularly violent crimes) are easier to dampen in locales with high existing crime rates, which is desirable result. But such conclusions are currently confounded by SES variables, and further work is needed to investigate these effects more thoroughly.

Extended Data Fig. 4 Stability of Suburban Bias over Years (Violent Crimes).

We show that the nature of the perturbation response shown in Fig. 3 holds true for earlier years as well: panels a and b correspond to year 2014, c and d correspond to 2015 and e and f correspond to year 2016, all of which follow the same pattern shown in Fig. 3.

Extended Data Fig. 5 Stability of Suburban Bias over Years (Property Crimes).

We show that the nature of the perturbation response shown in Fig. 3 holds true for earlier years as well: panels a and b correspond to year 2014, c and d correspond to 2015 and e and f correspond to year 2016, all of which follow the same pattern shown in Fig. 3.

Extended Data Fig. 6 Automatic Neighborhood Decomposition Using Event Predictability.

Using Event Predictability Computing a bi-clustering on the source-vs-target influence matrix (panel A) isolates a set of spatial tiles that are, on average, good predictors for all other tiles. Using this set, we use a Voronoi decomposition of the city (Panel B), which realizes an automatic spatial decomposition of the urban space, driven by event predictability.

Extended Data Table 1 Prediction Statistics for Portland. Prediction Statistics for the City of Portland, USA
Extended Data Table 2 Naive baseline results: mean AUC achieved with ARIMA models. Naive baseline results: mean AUC achieved with ARIMA models

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Supplementary Fig. 1 and methods.

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Rotaru, V., Huang, Y., Li, T. et al. Event-level prediction of urban crime reveals a signature of enforcement bias in US cities. Nat Hum Behav (2022). https://doi.org/10.1038/s41562-022-01372-0

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